from ._base import EncoderMixin from timm.models.regnet import RegNet import torch.nn as nn class RegNetEncoder(RegNet, EncoderMixin): def __init__(self, out_channels, depth=5, **kwargs): super().__init__(**kwargs) self._depth = depth self._out_channels = out_channels self._in_channels = 3 del self.head def get_stages(self): return [ nn.Identity(), self.stem, self.s1, self.s2, self.s3, self.s4, ] def forward(self, x): stages = self.get_stages() features = [] for i in range(self._depth + 1): x = stages[i](x) features.append(x) return features def load_state_dict(self, state_dict, **kwargs): state_dict.pop("head.fc.weight", None) state_dict.pop("head.fc.bias", None) super().load_state_dict(state_dict, **kwargs) regnet_weights = { "timm-regnetx_002": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_002-e7e85e5c.pth", # noqa }, "timm-regnetx_004": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_004-7d0e9424.pth", # noqa }, "timm-regnetx_006": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_006-85ec1baa.pth", # noqa }, "timm-regnetx_008": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_008-d8b470eb.pth", # noqa }, "timm-regnetx_016": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_016-65ca972a.pth", # noqa }, "timm-regnetx_032": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_032-ed0c7f7e.pth", # noqa }, "timm-regnetx_040": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_040-73c2a654.pth", # noqa }, "timm-regnetx_064": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_064-29278baa.pth", # noqa }, "timm-regnetx_080": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_080-7c7fcab1.pth", # noqa }, "timm-regnetx_120": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_120-65d5521e.pth", # noqa }, "timm-regnetx_160": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_160-c98c4112.pth", # noqa }, "timm-regnetx_320": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_320-8ea38b93.pth", # noqa }, "timm-regnety_002": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_002-e68ca334.pth", # noqa }, "timm-regnety_004": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_004-0db870e6.pth", # noqa }, "timm-regnety_006": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_006-c67e57ec.pth", # noqa }, "timm-regnety_008": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_008-dc900dbe.pth", # noqa }, "timm-regnety_016": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_016-54367f74.pth", # noqa }, "timm-regnety_032": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/regnety_032_ra-7f2439f9.pth", # noqa }, "timm-regnety_040": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_040-f0d569f9.pth", # noqa }, "timm-regnety_064": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_064-0a48325c.pth", # noqa }, "timm-regnety_080": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_080-e7f3eb93.pth", # noqa }, "timm-regnety_120": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_120-721ba79a.pth", # noqa }, "timm-regnety_160": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_160-d64013cd.pth", # noqa }, "timm-regnety_320": { "imagenet": "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_320-ba464b29.pth", # noqa }, } pretrained_settings = {} for model_name, sources in regnet_weights.items(): pretrained_settings[model_name] = {} for source_name, source_url in sources.items(): pretrained_settings[model_name][source_name] = { "url": source_url, "input_size": [3, 224, 224], "input_range": [0, 1], "mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225], "num_classes": 1000, } # at this point I am too lazy to copy configs, so I just used the same configs from timm's repo def _mcfg(**kwargs): cfg = dict(se_ratio=0.0, bottle_ratio=1.0, stem_width=32) cfg.update(**kwargs) return cfg timm_regnet_encoders = { "timm-regnetx_002": { "encoder": RegNetEncoder, "pretrained_settings": pretrained_settings["timm-regnetx_002"], "params": { "out_channels": (3, 32, 24, 56, 152, 368), "cfg": _mcfg(w0=24, wa=36.44, wm=2.49, group_w=8, depth=13), }, }, "timm-regnetx_004": { "encoder": RegNetEncoder, "pretrained_settings": pretrained_settings["timm-regnetx_004"], "params": { "out_channels": (3, 32, 32, 64, 160, 384), "cfg": _mcfg(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22), }, }, "timm-regnetx_006": { "encoder": RegNetEncoder, "pretrained_settings": pretrained_settings["timm-regnetx_006"], "params": { "out_channels": (3, 32, 48, 96, 240, 528), "cfg": _mcfg(w0=48, wa=36.97, wm=2.24, group_w=24, depth=16), }, }, "timm-regnetx_008": { "encoder": RegNetEncoder, "pretrained_settings": pretrained_settings["timm-regnetx_008"], "params": { "out_channels": (3, 32, 64, 128, 288, 672), "cfg": _mcfg(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16), }, }, "timm-regnetx_016": { "encoder": RegNetEncoder, "pretrained_settings": pretrained_settings["timm-regnetx_016"], "params": { "out_channels": (3, 32, 72, 168, 408, 912), "cfg": _mcfg(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18), }, }, "timm-regnetx_032": { "encoder": RegNetEncoder, "pretrained_settings": pretrained_settings["timm-regnetx_032"], "params": { "out_channels": (3, 32, 96, 192, 432, 1008), "cfg": _mcfg(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25), }, }, "timm-regnetx_040": { "encoder": RegNetEncoder, "pretrained_settings": pretrained_settings["timm-regnetx_040"], "params": { "out_channels": (3, 32, 80, 240, 560, 1360), "cfg": _mcfg(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23), }, }, "timm-regnetx_064": { "encoder": RegNetEncoder, "pretrained_settings": pretrained_settings["timm-regnetx_064"], "params": { "out_channels": (3, 32, 168, 392, 784, 1624), "cfg": _mcfg(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17), }, }, "timm-regnetx_080": { "encoder": RegNetEncoder, "pretrained_settings": pretrained_settings["timm-regnetx_080"], "params": { "out_channels": (3, 32, 80, 240, 720, 1920), "cfg": _mcfg(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23), }, }, "timm-regnetx_120": { "encoder": RegNetEncoder, "pretrained_settings": pretrained_settings["timm-regnetx_120"], "params": { "out_channels": (3, 32, 224, 448, 896, 2240), "cfg": _mcfg(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19), }, }, "timm-regnetx_160": { "encoder": RegNetEncoder, "pretrained_settings": pretrained_settings["timm-regnetx_160"], "params": { "out_channels": (3, 32, 256, 512, 896, 2048), "cfg": _mcfg(w0=216, wa=55.59, wm=2.1, group_w=128, depth=22), }, }, "timm-regnetx_320": { "encoder": RegNetEncoder, "pretrained_settings": pretrained_settings["timm-regnetx_320"], "params": { "out_channels": (3, 32, 336, 672, 1344, 2520), "cfg": _mcfg(w0=320, wa=69.86, wm=2.0, group_w=168, depth=23), }, }, # regnety "timm-regnety_002": { "encoder": RegNetEncoder, "pretrained_settings": pretrained_settings["timm-regnety_002"], "params": { "out_channels": (3, 32, 24, 56, 152, 368), "cfg": _mcfg(w0=24, wa=36.44, wm=2.49, group_w=8, depth=13, se_ratio=0.25), }, }, "timm-regnety_004": { "encoder": RegNetEncoder, "pretrained_settings": pretrained_settings["timm-regnety_004"], "params": { "out_channels": (3, 32, 48, 104, 208, 440), "cfg": _mcfg(w0=48, wa=27.89, wm=2.09, group_w=8, depth=16, se_ratio=0.25), }, }, "timm-regnety_006": { "encoder": RegNetEncoder, "pretrained_settings": pretrained_settings["timm-regnety_006"], "params": { "out_channels": (3, 32, 48, 112, 256, 608), "cfg": _mcfg(w0=48, wa=32.54, wm=2.32, group_w=16, depth=15, se_ratio=0.25), }, }, "timm-regnety_008": { "encoder": RegNetEncoder, "pretrained_settings": pretrained_settings["timm-regnety_008"], "params": { "out_channels": (3, 32, 64, 128, 320, 768), "cfg": _mcfg(w0=56, wa=38.84, wm=2.4, group_w=16, depth=14, se_ratio=0.25), }, }, "timm-regnety_016": { "encoder": RegNetEncoder, "pretrained_settings": pretrained_settings["timm-regnety_016"], "params": { "out_channels": (3, 32, 48, 120, 336, 888), "cfg": _mcfg(w0=48, wa=20.71, wm=2.65, group_w=24, depth=27, se_ratio=0.25), }, }, "timm-regnety_032": { "encoder": RegNetEncoder, "pretrained_settings": pretrained_settings["timm-regnety_032"], "params": { "out_channels": (3, 32, 72, 216, 576, 1512), "cfg": _mcfg(w0=80, wa=42.63, wm=2.66, group_w=24, depth=21, se_ratio=0.25), }, }, "timm-regnety_040": { "encoder": RegNetEncoder, "pretrained_settings": pretrained_settings["timm-regnety_040"], "params": { "out_channels": (3, 32, 128, 192, 512, 1088), "cfg": _mcfg(w0=96, wa=31.41, wm=2.24, group_w=64, depth=22, se_ratio=0.25), }, }, "timm-regnety_064": { "encoder": RegNetEncoder, "pretrained_settings": pretrained_settings["timm-regnety_064"], "params": { "out_channels": (3, 32, 144, 288, 576, 1296), "cfg": _mcfg( w0=112, wa=33.22, wm=2.27, group_w=72, depth=25, se_ratio=0.25 ), }, }, "timm-regnety_080": { "encoder": RegNetEncoder, "pretrained_settings": pretrained_settings["timm-regnety_080"], "params": { "out_channels": (3, 32, 168, 448, 896, 2016), "cfg": _mcfg( w0=192, wa=76.82, wm=2.19, group_w=56, depth=17, se_ratio=0.25 ), }, }, "timm-regnety_120": { "encoder": RegNetEncoder, "pretrained_settings": pretrained_settings["timm-regnety_120"], "params": { "out_channels": (3, 32, 224, 448, 896, 2240), "cfg": _mcfg( w0=168, wa=73.36, wm=2.37, group_w=112, depth=19, se_ratio=0.25 ), }, }, "timm-regnety_160": { "encoder": RegNetEncoder, "pretrained_settings": pretrained_settings["timm-regnety_160"], "params": { "out_channels": (3, 32, 224, 448, 1232, 3024), "cfg": _mcfg( w0=200, wa=106.23, wm=2.48, group_w=112, depth=18, se_ratio=0.25 ), }, }, "timm-regnety_320": { "encoder": RegNetEncoder, "pretrained_settings": pretrained_settings["timm-regnety_320"], "params": { "out_channels": (3, 32, 232, 696, 1392, 3712), "cfg": _mcfg( w0=232, wa=115.89, wm=2.53, group_w=232, depth=20, se_ratio=0.25 ), }, }, }